Multi-source data fusion converter installation buggy ladle positioning and driving method

Abstract

The invention discloses a multi-source data fusion converter installation buggy ladle positioning and driving method, and belongs to the technical field of positioning and driving. The method comprises the following steps: S1, acquiring position point cloud data of a buggy ladle to obtain original point cloud data, and setting a target point cloud; s2, performing point cloud registration on the original point cloud data obtained in the step S1; s3, solving the center distance deviation between the position of the buggy ladle and the expected target position; and S4, judging whether the buggy ladle arrives at an expected position or not by utilizing the center distance deviation solved in the step S3, and further controlling a buggy ladle mechanism to displace. According to the method, the point cloud registration is carried out on the point cloud data, the center distance deviation value is calculated, and the center distance deviation value is corrected, so that the accuracy of accurate positioning is effectively improved, and the target position is more efficiently reached.

Description

Technical Field

[0001] The invention belongs to the technical field of positioning drive, and more specifically, relates to a positioning drive method for a converter mounted ladle car with multi-source data fusion. Background Art

[0002] Positioning drive systems are widely used in industrial production to avoid economic losses caused by position deviation of equipment due to manual operation. In the installation process of existing large-scale converters, it is very common to use ladle cars for converter installation, but the ladle cars are usually roughly judged by humans during the movement of the ladle cars. In this process, the ladle cars are prone to position deviations from the expected position due to errors in human judgment, resulting in greater safety hazards and increased costs of manpower and material resources.

[0003] After searching, patent CN117788538A discloses a registration method, device and system for point cloud interval pairing volume variance consistency, the method comprising: downsampling the point cloud data to be registered, selecting any point in the point cloud data to be registered after the downsampling processing as the center point, and using the k nearest neighbor algorithm to find the k points closest to the center point; calculating the local directional centrality measure for each selected center point and the k points around the center point to obtain the local directional centrality measure values ​​of all points in the source point cloud and the target point cloud; sorting the local directional centrality measure values, finding the maximum and minimum values, dividing the maximum and minimum values ​​into equal intervals to obtain a number of intervals; counting the number of local directional centrality measure values ​​in each interval, and calculating the ratio of the number of local directional centrality measure values ​​in each interval to the total number of local directional centrality measure values; comparing the ratios to determine the registration accuracy.

[0004] Patent CN110356976A discloses a precise alignment system and method for unmanned container truck cranes in ports based on template matching, wherein the system includes a precise positioning device, an alignment computer and an unmanned container truck controller; the precise positioning device is connected to the unmanned container truck controller and is used to collect point cloud observation data of the current container truck in real time; the alignment computer is connected to the unmanned container truck controller and is used to obtain an alignment result of the error between the actual position of the container truck and the target position of the container truck based on a preset crane feature template and an extended Kalman filtering method; the unmanned container truck controller is used to accurately align the container truck and the crane according to the error between the actual position of the container truck and the target position of the container truck in the alignment result.

[0005] However, in the above patent, the point cloud data is not calibrated when calculating the deviation from the center value, so the accuracy is not high. Summary of the invention

[0006] 1. Problem to be solved

[0007] In view of the problem of low positioning accuracy of existing ladle cars, the present invention provides a positioning and driving method for a converter-mounted ladle car by fusion of multi-source data, which effectively improves the positioning accuracy of the ladle car.

[0008] 2. Technical solution

[0009] In order to solve the above problems, the technical solution adopted by the present invention is as follows:

[0010] A method for positioning and driving a ladle car installed in a converter by fusion of multi-source data of the present invention comprises the following steps:

[0011] Step S1: collecting the position point cloud data of the ladle car during the movement, obtaining the original point cloud data, and setting the target point cloud;

[0012] Step S2: performing point cloud registration on the point cloud data obtained in step S1 to create a complete model that integrates data collected from different perspectives;

[0013] Step S3: solving the center distance deviation between the position of the ladle car and the expected target position;

[0014] Step S4: Use the center distance deviation solved in step S3 to determine whether the ladle car has reached the expected position, and then control the ladle car mechanism to move.

[0015] Furthermore, in step S1, a laser scanner is used to acquire point cloud data of the position of the ladle car, wherein the target point cloud is set according to the position actually required for positioning.

[0016] Furthermore, the specific method of point cloud registration in step S2 is:

[0017] Step S101: Select the original point cloud data and the corresponding target point cloud data: To perform three-dimensional point cloud registration, at least three groups of corresponding point pairs are required. When three groups of corresponding point pairs are selected, the three points must be non-collinear points; when more than three groups of corresponding point pairs are selected, any three points in these corresponding point pairs must meet the non-collinear requirement. Therefore, it is necessary to select three non-collinear data points {m1, m2, m3} from the original point cloud M obtained by scanning the ladle car with a laser scanner, and then select {n1, n2, n3} from the target point cloud N, where m1 corresponds to n1, m2 corresponds to n2, and m3 corresponds to n3.

[0018] Step S102: transforming the original point cloud M into a new three-dimensional point cloud data set M′{m1′,m2′,m3′} according to the rotation and translation matrix H;

[0019] Among them, the calculation process of the rotation and translation matrix H is:

[0020] The translation matrix t is:

[0021] The rotation matrix R is divided into R when rotating around the X axis x (θ), R when rotating around the Y axis y (θ), R when rotating around the Z axis Z (θ), where:

[0022] Rotation around the X axis:

[0023] Rotation around the Y axis:

[0024] Rotation around the Z axis:

[0025] When calculating the rotation and translation matrix H, R x (θ), R y (θ) and R Z (θ) is brought into the formula of the rotation and translation matrix H, that is: That is, the rotation and translation matrices are obtained when rotating around the X-axis, Y-axis, and Z-axis respectively.

[0026] Step S103: transform the target point cloud N into a new three-dimensional point cloud data set N'{n'1, n'2, n'3} according to the point cloud matching formula;

[0027] The formula used for point cloud matching is:

[0028] Where M is the original point cloud, i.e. the original point cloud data, and M i and N i It is the corresponding point in the original point cloud M and the target point cloud N after transformation i times, R is the rotation matrix, t is the translation matrix, and m is the number of transformations.

[0029] Step S104: Calculate the distance between M′ and N′, extract all points whose distance is less than a given threshold t to form a consistent point cloud set S1, and record the number of internal points P1 in the set S1;

[0030] The distance between M' and N' is calculated as:

[0031]

[0032] Step S105: In order to prevent the model parameter estimation process from falling into an infinite loop, a threshold S needs to be set. When the iterative execution times of step S101, step S102, step S103 and step S104 reach S times, the consistency set cannot be obtained, and the model parameter estimation fails; on the contrary, if the consistency set is found before the iterative execution times of the first four steps reach S times, then a consistency set with the largest number of data points, that is, the consistency set with the largest number of internal points P1, is selected from the found consistency sets, and then all the points in this largest consistency set are subjected to the final model parameter estimation by using the least squares method, and the corresponding rigid body transformation matrix is ​​solved. The rigid body transformation rectangle is the point cloud for calculating the center distance deviation.

[0033] Step S2: Calculate the center distance deviation: After the point cloud data is aligned, the center distance deviation between the position of the ladle car and the expected target position is calculated. The specific steps are as follows:

[0034] Step S201: Select the point cloud data after point cloud registration, that is, the rigid body transformation matrix obtained in step S106, define the reference point cloud and the comparison point cloud, where the comparison point cloud is the point cloud used to calculate the distance, then calculate the distance of each point in the comparison point cloud relative to the reference point cloud, generate a scalar field color difference according to the distance, and finally display it on the comparison point cloud;

[0035] Step S202: Calculate approximate distances, at which point the reference point cloud is hidden and the comparison point cloud is colored using these approximate distances;

[0036] The steps for calculating the approximate distance are as follows: calculating the distance between the center point and each sample point, sorting the calculated distances between the center point and each sample point, and selecting the point with the smallest approximate distance.

[0037] Step S203: When the local model is not used at this time, the distance between point clouds is the nearest neighbor distance; if local modeling is used, a local model will be calculated around the nearest point to approximate the real surface and obtain a more accurate center distance deviation value estimate. The center distance deviation value calculation method includes the steps of:

[0038] a. Project the point cloud onto the fitted ground plane according to the local model and extract the boundary of the two-dimensional point cloud;

[0039] b. Use the least squares method to fit the points on the boundary of the two-dimensional point cloud. The fitting result can be approximately regarded as the center line of the original point cloud.

[0040] c. Use the fitted straight line as the new x-axis to establish a new coordinate system, and transform the coordinates of all point clouds. The transformed point cloud coordinates are {x i ,y i ,z i}, the point cloud coordinate matrix is:

[0041]

[0042] d. Calculate the distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation, and take the minimum value d m ;

[0043] e. The distance from the projected point cloud fitting midline to the original point cloud is recorded as d x , d x The calculation formula is:

[0044]

[0045] h is the height of the original point cloud from the fitted ground plane, θ is the tilt ratio of the original point cloud, r is the width of the point cloud after coordinate transformation, d r is the distance between the fitted midline and the point cloud after coordinate transformation, d m To calculate the minimum distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation;

[0046] f. According to the final midline fitting result, the distance from the origin cloud to the target point, that is, the center distance deviation, can be calculated as follows. The final corrected distance is: Where d m To calculate the minimum distance from all points in the point cloud to the new coordinate system XOZ plane after coordinate transformation, d x It is the distance from the midline of the projected point cloud to the original point cloud.

[0047] In step S3, the center distance deviation value obtained in step S2 is used to determine whether the ladle car is accurately positioned and then control the displacement of the mechanism. The process is as follows:

[0048] Step S301: First, judging according to the center distance deviation value obtained in step S2, comparing the center distance deviation value with the estimated range value, and judging whether the center distance deviation value is within a reasonable range;

[0049] Step S302: When the center distance deviation value meets the requirement, that is, the ladle car is accurately positioned successfully, the ladle car stops moving and waits for the next operation;

[0050] When the center distance deviation value does not meet the requirements, it is necessary to displace the mechanism according to the deviation value obtained after processing the point cloud data. After the displacement, steps S1-S3 need to be repeated to calculate the center distance deviation after the control mechanism is displaced, and determine whether the center distance deviation value at this time meets the requirements.

[0051] The present invention mainly uses a laser scanner to scan the trajectory of the ladle car during movement to obtain corresponding point cloud data, then aligns the obtained point cloud to determine the center distance deviation of the ladle car's movement distance, and finally utilizes the hydraulic cylinder extension of the ladle car's power structure to push the ladle car to move back and forth along a fixed track, thereby enabling the ladle car to accurately move to the expected position.

[0052] Since in the fitting process, the boundary points of the two-dimensional point cloud are fitted with straight lines, but the distribution of these boundary points is irregular and not completely on a straight line, the present application uses the least squares method to approximate the midline of the original point cloud in the calculation process of the center distance deviation value, establishes a new coordinate system, and corrects the center distance deviation value to reduce the error caused by data fitting in the fitting process and improve positioning accuracy.

[0053] The present invention adopts multi-source data fusion, and through the joint action of multi-source data, the accuracy of positioning can be greatly improved.

[0054] The use of multiple data to position and drive the ladle car makes up for the inaccurate positioning and position deviation that occurs when the ladle car is positioned manually under human judgment in the traditional way. It can effectively improve the positioning accuracy and reduce the deviation from the expected position. The improved positioning and driving method is applied in the installation process of large-scale converters. On the one hand, it can improve the installation efficiency of the converter; on the other hand, the position deviation can be selectively adjusted during the movement of the ladle car to reduce the influence of human judgment.

[0055] 3. Beneficial effects

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) A multi-source data fusion converter installation ladle car positioning and driving method of the present invention uses a laser scanner to scan and obtain point cloud data of the ladle car, and performs point cloud registration on the point cloud data to calculate the center distance deviation value. Compared with the method before improvement, the positioning and driving method has enhanced accuracy in precise positioning and can reach the target position more efficiently;

[0058] (2) In the method for positioning and driving a ladle car installed in a converter by fusion of multi-source data of the present invention, in the process of calculating the center distance deviation value, the least square method is used to simulate the center line of the original point cloud, a new coordinate system is established, and the center deviation of the point cloud is corrected to improve the accuracy of positioning;

[0059] (3) A method for positioning and driving a ladle car installed in a converter by fusion of multi-source data of the present invention utilizes the characteristics of multi-source data to not only obtain more comprehensive position information of the ladle car, but also improve the accuracy of the ladle car reaching the target position;

[0060] (4) The multi-source data fusion converter installation ladle car positioning and driving method of the present invention still has good performance in other environments, can well solve the problem of positioning and driving of mobile devices in reality and has good generalization ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but it should be understood that these drawings are designed only for explanation purposes and are not intended to limit the scope of the present invention. In addition, unless otherwise specified, these drawings are intended only to conceptually illustrate the structural configurations described herein and are not necessarily drawn to scale.

[0062] Figure 1 The present invention is a flow chart of a method for positioning and driving a ladle car for converter installation using multi-source data fusion. DETAILED DESCRIPTION

[0063] The following detailed description of exemplary embodiments of the present invention refers to the accompanying drawings, which form a part of the description, and in which exemplary embodiments of the present invention that can be implemented are shown as examples. Although these exemplary embodiments are described in sufficient detail to enable those skilled in the art to implement the present invention, it should be understood that other embodiments can be implemented and various changes can be made to the present invention without departing from the spirit and scope of the present invention. The following more detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but is only for the purpose of illustrating and not limiting the description of the characteristics and features of the present invention, so as to propose the best mode for performing the present invention and to enable those skilled in the art to implement the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

[0064] Example

[0065] A method for positioning and driving a ladle car installed in a converter by fusion of multi-source data of the present invention comprises the following steps:

[0066] Step S1: collecting the position point cloud data of the ladle car during the movement, obtaining the original point cloud data, and setting the target point cloud;

[0067] Step S2: performing point cloud registration on the point cloud data obtained in step S1 to create a complete model that integrates data collected from different perspectives;

[0068] Step S3: solving the center distance deviation between the position of the ladle car and the expected target position;

[0069] Step S4: Use the center distance deviation solved in step S3 to determine whether the ladle car has reached the expected position, and then control the ladle car mechanism to move.

[0070] Furthermore, in step S1, a laser scanner is used to acquire point cloud data of the position of the ladle car, wherein the target point cloud is set according to the position actually required for positioning.

[0071] Furthermore, the specific method of point cloud registration in step S2 is:

[0072] Step S101: Select the original point cloud data and the corresponding target point cloud data: To perform three-dimensional point cloud registration, at least three groups of corresponding point pairs are required. When three groups of corresponding point pairs are selected, the three points must be non-collinear points; when more than three groups of corresponding point pairs are selected, any three points in these corresponding point pairs must meet the non-collinear requirement. Therefore, it is necessary to select three non-collinear data points {m1, m2, m3} from the original point cloud M obtained by scanning the ladle car with a laser scanner, and then select {n1, n2, n3} from the target point cloud N, where m1 corresponds to n1, m2 corresponds to n2, and m3 corresponds to n3.

[0073] Step S102: transforming the original point cloud M into a new three-dimensional point cloud data set M′{m1′,m2′,m3′} according to the rotation and translation matrix H;

[0074] Among them, the rotation and translation matrix H is calculated based on the translation matrix t and the rotation matrix R.

[0075] The translation matrix t is:

[0076] The rotation matrix R is: Rotation around the X axis:

[0077] Rotation around the Y axis:

[0078] Rotation around the Z axis:

[0079] When calculating the rotation and translation matrix H, R x (θ), R y (θ) and R Z (θ) is brought into the formula of the rotation and translation matrix H, that is: That is, the rotation and translation matrices are obtained when rotating around the X-axis, Y-axis, and Z-axis respectively.

[0080] Step S103: transform the target point cloud N into a new three-dimensional point cloud data set N'{n'1, n'2, n'3} according to the point cloud matching formula;

[0081] The formula used for point cloud matching is:

[0082] In the formula, M is the original point cloud data, M i and N iIt is the corresponding point in the original point cloud data M and the target point cloud N after transformation i times, R is the rotation matrix, t is the translation matrix, and m is the number of transformations.

[0083] Step S104: Calculate the distance between M′ and N′, extract all points whose distance is less than a given threshold t to form a consistent point cloud set S1, and record the number of internal points P1 in the set S1;

[0084] The distance between M' and N' is calculated as:

[0085]

[0086] Step S105: In order to prevent the model parameter estimation process from falling into an infinite loop, a threshold S needs to be set. When the iterative execution times of step S101, step S102, step S103 and step S104 reach S times, the consistency set cannot be obtained, and the model parameter estimation fails. On the contrary, if the consistency set is found before the iterative execution times of the first four steps reach S times, a consistency set with the largest number of data points is selected from the found consistency sets, and then all the points in this largest consistency set are subjected to the final model parameter estimation by using the least squares method, and the corresponding rigid body transformation matrix is ​​solved. The rigid body transformation rectangle is the point cloud for calculating the center distance deviation.

[0087] Step S2: Calculate the center distance deviation: After the point cloud data is aligned, the center distance deviation between the position of the ladle car and the expected target position is calculated. The specific steps are as follows:

[0088] Step S201: Select the point cloud data after point cloud registration, that is, the rigid body transformation matrix obtained in step S106, define the reference point cloud and the comparison point cloud, where the comparison point cloud is the point cloud used to calculate the distance, then calculate the distance of each point in the comparison point cloud relative to the reference point cloud, generate a scalar field color difference according to the distance, and finally display it on the comparison point cloud;

[0089] Step S202: Calculate approximate distances, at which point the reference point cloud is hidden and the comparison point cloud is colored using these approximate distances;

[0090] The steps for calculating the approximate distance are as follows: calculating the distance between the center point and each sample point, sorting the calculated distances between the center point and each sample point, and selecting the point with the smallest approximate distance.

[0091] Step S203: When the local model is not used at this time, the distance between point clouds is the nearest neighbor distance; if local modeling is used, a local model will be calculated around the nearest point to approximate the real surface and obtain a more accurate center distance deviation value estimate. The center distance deviation value calculation method includes the steps of:

[0092] a. Project the point cloud onto the fitted ground plane according to the local model and extract the boundary of the two-dimensional point cloud;

[0093] b. Use the least squares method to fit the points on the boundary of the two-dimensional point cloud. The fitting result can be approximately regarded as the center line of the original point cloud.

[0094] c. Use the fitted straight line as the new x-axis to establish a new coordinate system, and transform the coordinates of all point clouds. The transformed point cloud coordinates are {x i ,y i ,z i}, the point cloud coordinate matrix is:

[0095]

[0096] d. Calculate the distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation, and take the minimum value d m ;

[0097] e. The distance from the projected point cloud fitting midline to the original point cloud is recorded as d x , d x The calculation formula is:

[0098]

[0099] h is the height of the original point cloud from the fitted ground plane, θ is the tilt ratio of the original point cloud, r is the width of the point cloud after coordinate transformation, d r is the distance between the fitted midline and the point cloud after coordinate transformation, d m To calculate the minimum distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation;

[0100] f. The final corrected distance is: Where d m To calculate the minimum distance from all points in the point cloud to the new coordinate system XOZ plane after coordinate transformation, d x It is the distance from the midline of the projected point cloud to the original point cloud.

[0101] In step S3, the center distance deviation value obtained in step S2 is used to determine whether the ladle car is accurately positioned and then control the displacement of the mechanism. The process is as follows:

[0102] Step S301: First, judging according to the center distance deviation value obtained in step S2, comparing the center distance deviation value with the estimated range value, and judging whether the center distance deviation value is within a reasonable range;

[0103] Step S302: When the center distance deviation value meets the requirement, that is, the ladle car is accurately positioned successfully, the ladle car stops moving and waits for the next operation;

[0104] When the center distance deviation value does not meet the requirements, it is necessary to displace the mechanism according to the deviation value obtained after processing the point cloud data. After the displacement, steps S1-S3 need to be repeated to calculate the center distance deviation after the control mechanism is displaced, and determine whether the center distance deviation value at this time meets the requirements.

[0105] Generally speaking, the center distance deviation value is set within the range of ±2mm%. Within this range, the center distance deviation value is reasonable, that is, the ladle car is accurately positioned successfully. At this time, the ladle car stops moving and waits for the next operation.

[0106] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope involved in the present application is not limited to the technical solution formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the inventive concept, such as a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0107] Except for the technical features described in the specification, the remaining technical features are known technologies to those skilled in the art. In order to highlight the innovative features of the present invention, the remaining technical features will not be described here in detail.

Claims

1. A method for positioning and driving a ladle car installed in a converter by fusion of multi-source data, characterized in that: The following steps are involved: Step S1: Collect the position point cloud data of the ladle car, obtain the original point cloud M, and set the target point cloud N; Step S2: performing point cloud registration on the point cloud data obtained in step S1; Step S3: solving the center distance deviation between the position of the ladle car and the expected target position; Step S4: Use the center distance deviation solved in step S3 to determine whether the ladle car has reached the expected position, and then control the ladle car to move.

2. The method according to claim 1, characterized in that The specific steps of point cloud registration in step S2 include: Step S101: Select data points {m1, m2, m3} from the original point cloud M, and then select data points {n1, n2, n3} from the target point cloud N; Step S102: transforming the original point cloud M into a new three-dimensional point cloud data set M′{m′1, m′2, m′3} according to the rotation and translation matrix H; Step S103: transform the target point cloud N into a new three-dimensional point cloud data set N'{n'1, n'2, n'3} according to the point cloud matching formula; Step S104: Calculate the distance between M′ and N′, extract all points whose distance is less than a given threshold t to form a consistent point cloud set S1, and record the number of internal points P1 of the set S1; Step S105: iteratively execute steps S101-S104 to find the consistent set with the most internal points; Step S106: The points in the consistent point cloud set S1 obtained in step S105 are estimated using the least square method for model parameter estimation to solve the corresponding rigid body transformation matrix, which is the point cloud data after point cloud registration.

3. The method according to claim 2, characterized in that The number of the data points {m1, m2, m3} is three or more non-collinear data points.

4. The method according to claim 2, characterized in that: The rotation and translation matrix H is: Where R is the rotation matrix and t is the translation matrix.

5. The method according to claim 2, characterized in that: The formula used for point cloud matching is: Where M is the origin cloud, M i and N i It is the corresponding point in the original point cloud M and the target point cloud N after transformation i times, R is the rotation matrix, t is the translation matrix, and m is the number of transformations.

6. The method according to claim 2, characterized in that Step S3: The process of solving the center distance deviation includes the following steps: Step S201: defining a reference point cloud and a comparison point cloud in the point cloud data obtained after the point cloud registration in step S106; Step S202: Calculate the approximate distance; Step S203: Select the point with the closest approximate distance, establish a local model around it, and calculate the center distance deviation value.

7. The method according to claim 6, characterized in that The step of calculating the approximate distance in step S202 is: calculating the distance between the center point and each sample point, sorting the calculated distances between the center point and each sample point, and selecting the point with the smallest approximate distance.

8. The method according to claim 7, characterized in that The calculation method of the center distance deviation value is: a. Project the point cloud onto the fitted ground plane according to the local model and extract the boundary of the two-dimensional point cloud; b. Use the least squares method to perform straight line fitting on the points on the boundary of the two-dimensional point cloud, and the fitting result is the center line of the original point cloud; c. Establish a new coordinate system with the straight line fitted in step b as the new x-axis, and transform the coordinates of all point clouds. The coordinates of the transformed point clouds are {x i ,y i ,z i }, the point cloud coordinate matrix is: d. Calculate the distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation, and take the minimum value d m ; e. The distance from the projected point cloud fitting midline to the original point cloud is recorded as d x , d x The calculation formula is: Where h is the height of the original point cloud from the fitted ground plane, θ is the tilt ratio of the original point cloud, r is the width of the point cloud after coordinate transformation, and d is r is the distance between the fitted midline and the point cloud after coordinate transformation, d m To calculate the minimum distance from all points in the point cloud to the XOZ plane of the new coordinate system after coordinate transformation; f. The final corrected center distance deviation is: Where d m To calculate the minimum distance from all points in the point cloud to the new coordinate system XOZ plane after coordinate transformation, d x It is the distance from the midline of the projected point cloud to the original point cloud.

9. The method according to claim 8, characterized in that The process of determining whether the ladle car is accurately positioned in step S3 is as follows: Step S301: Compare the center distance deviation value obtained in step S2 with the estimated range value to determine whether the center distance deviation value is within a reasonable range; Step S302: When the center distance deviation value meets the requirement, that is, the ladle car is accurately positioned successfully, the ladle car stops moving and waits for the next step.

10. The method according to claim 9, characterized in that When the center distance deviation value in step S302 does not meet the requirements, the mechanism is displaced according to the deviation value obtained after point cloud data processing. After the displacement, steps S1-S3 are repeated to calculate the center distance deviation after the control mechanism is displaced, and determine whether the center distance deviation value at this time meets the requirements.

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